Department of Electronic Engineering, City University of Hong Kong, Kowloon, Hong Kong.
Proteome Sci. 2011 Oct 14;9 Suppl 1(Suppl 1):S1. doi: 10.1186/1477-5956-9-S1-S1.
Previous studies on protein-DNA interaction mostly focused on the bound structure of DNA-binding proteins but few paid enough attention to the unbound structures. As more new proteins are discovered, it is useful and imperative to develop algorithms for the functional prediction of unbound proteins. In our work, we apply an alpha shape model to represent the surface structure of the protein-DNA complex and extract useful statistical and geometric features, and use structural alignment and support vector machines for the prediction of unbound DNA-binding proteins.
The performance of our method is evaluated by discriminating a set of 104 DNA-binding proteins from 401 non-DNA-binding proteins. In the same test, the proposed method outperforms the other method using conditional probability. The results achieved by our proposed method for; precision, 83.33%; accuracy, 86.53%; and MCC, 0.5368 demonstrate its good performance.
In this study we develop an effective method for the prediction of protein-DNA interactions based on statistical and geometric features and support vector machines. Our results show that interface surface features play an important role in protein-DNA interaction. Our technique is able to predict unbound DNA-binding protein and discriminatory DNA-binding proteins from proteins that bind with other molecules.
先前关于蛋白质与 DNA 相互作用的研究大多集中在 DNA 结合蛋白的结合结构上，但很少有研究关注未结合结构。随着越来越多的新蛋白质被发现，开发用于预测未结合蛋白质功能的算法是有用且必要的。在我们的工作中，我们应用了一种 alpha 形状模型来表示蛋白质-DNA 复合物的表面结构，并提取了有用的统计和几何特征，然后使用结构对齐和支持向量机进行未结合 DNA 结合蛋白的预测。
通过将 104 个 DNA 结合蛋白与 401 个非 DNA 结合蛋白区分开来，评估了我们方法的性能。在相同的测试中，我们提出的方法优于使用条件概率的其他方法。我们提出的方法在预测精度、准确率和 MCC 方面的结果分别为 83.33%、86.53%和 0.5368，证明了其良好的性能。
在这项研究中，我们开发了一种基于统计和几何特征以及支持向量机的有效预测蛋白质-DNA 相互作用的方法。我们的结果表明，界面表面特征在蛋白质-DNA 相互作用中起着重要作用。我们的技术能够预测未结合的 DNA 结合蛋白，并区分与其他分子结合的蛋白质中的 DNA 结合蛋白。
